Overview

Dataset statistics

Number of variables9
Number of observations2820
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory398.6 KiB
Average record size in memory144.8 B

Variable types

Numeric9

Alerts

average_size_of_households is highly overall correlated with one_person_households and 1 other fieldsHigh correlation
average_floor_area_per_person is highly overall correlated with apartments_ratio and 4 other fieldsHigh correlation
one_person_households is highly overall correlated with average_size_of_households and 4 other fieldsHigh correlation
with_children is highly overall correlated with average_size_of_households and 2 other fieldsHigh correlation
pensioner_households is highly overall correlated with average_floor_area_per_person and 1 other fieldsHigh correlation
owner_occupied is highly overall correlated with apartments_ratio and 4 other fieldsHigh correlation
rented is highly overall correlated with apartments_ratio and 4 other fieldsHigh correlation
small_houses_ratio is highly overall correlated with apartments_ratio and 4 other fieldsHigh correlation
apartments_ratio is highly overall correlated with average_floor_area_per_person and 3 other fieldsHigh correlation
rented has 63 (2.2%) zerosZeros
apartments_ratio has 1249 (44.3%) zerosZeros

Reproduction

Analysis started2024-05-31 14:43:48.700152
Analysis finished2024-05-31 14:43:53.400859
Duration4.7 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

average_size_of_households
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0665248
Minimum1
Maximum3.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size222.4 KiB
2024-05-31T16:43:53.448298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.6
Q11.9
median2
Q32.225
95-th percentile2.6
Maximum3.9
Range2.9
Interquartile range (IQR)0.325

Descriptive statistics

Standard deviation0.30250224
Coefficient of variation (CV)0.1463821
Kurtosis1.5838608
Mean2.0665248
Median Absolute Deviation (MAD)0.2
Skewness0.6465602
Sum5827.6
Variance0.091507607
MonotonicityNot monotonic
2024-05-31T16:43:53.509144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2 405
14.4%
2.1 383
13.6%
1.9 361
12.8%
1.8 329
11.7%
2.2 286
10.1%
2.3 247
8.8%
1.7 186
6.6%
2.4 179
6.3%
2.5 105
 
3.7%
1.6 79
 
2.8%
Other values (16) 260
9.2%
ValueCountFrequency (%)
1 1
 
< 0.1%
1.3 4
 
0.1%
1.4 26
 
0.9%
1.5 55
 
2.0%
1.6 79
 
2.8%
1.7 186
6.6%
1.8 329
11.7%
1.9 361
12.8%
2 405
14.4%
2.1 383
13.6%
ValueCountFrequency (%)
3.9 1
 
< 0.1%
3.7 1
 
< 0.1%
3.6 1
 
< 0.1%
3.5 1
 
< 0.1%
3.4 3
 
0.1%
3.3 3
 
0.1%
3.1 3
 
0.1%
3 7
 
0.2%
2.9 11
0.4%
2.8 23
0.8%

average_floor_area_per_person
Real number (ℝ)

HIGH CORRELATION 

Distinct335
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.686773
Minimum22.7
Maximum71.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size222.4 KiB
2024-05-31T16:43:53.581966image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum22.7
5-th percentile36.7
Q143.7
median47.9
Q351.7
95-th percentile57.9
Maximum71.9
Range49.2
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.3805591
Coefficient of variation (CV)0.13380144
Kurtosis0.21858672
Mean47.686773
Median Absolute Deviation (MAD)4
Skewness-0.1329863
Sum134476.7
Variance40.711535
MonotonicityNot monotonic
2024-05-31T16:43:53.649148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 31
 
1.1%
47.2 29
 
1.0%
47.8 27
 
1.0%
51.3 26
 
0.9%
50 25
 
0.9%
49.2 25
 
0.9%
48.6 25
 
0.9%
47 24
 
0.9%
48.4 24
 
0.9%
47.9 24
 
0.9%
Other values (325) 2560
90.8%
ValueCountFrequency (%)
22.7 1
< 0.1%
25 1
< 0.1%
28.2 1
< 0.1%
29.6 1
< 0.1%
29.7 1
< 0.1%
29.8 2
0.1%
30.1 1
< 0.1%
30.5 2
0.1%
30.8 2
0.1%
30.9 2
0.1%
ValueCountFrequency (%)
71.9 1
 
< 0.1%
70.5 1
 
< 0.1%
67.8 1
 
< 0.1%
66.6 1
 
< 0.1%
66 1
 
< 0.1%
65.1 2
0.1%
64.6 3
0.1%
64.5 1
 
< 0.1%
64.3 2
0.1%
64.1 1
 
< 0.1%

one_person_households
Real number (ℝ)

HIGH CORRELATION 

Distinct2244
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39157027
Minimum0.11802575
Maximum0.96774194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size222.4 KiB
2024-05-31T16:43:53.718666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.11802575
5-th percentile0.22268316
Q10.31398208
median0.38849031
Q30.46156918
95-th percentile0.57804593
Maximum0.96774194
Range0.84971618
Interquartile range (IQR)0.1475871

Descriptive statistics

Standard deviation0.10863967
Coefficient of variation (CV)0.27744615
Kurtosis0.21196048
Mean0.39157027
Median Absolute Deviation (MAD)0.074204595
Skewness0.33799379
Sum1104.2282
Variance0.011802577
MonotonicityNot monotonic
2024-05-31T16:43:53.875706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 26
 
0.9%
0.3333333333 24
 
0.9%
0.4 18
 
0.6%
0.3636363636 13
 
0.5%
0.375 10
 
0.4%
0.4210526316 9
 
0.3%
0.3846153846 9
 
0.3%
0.25 9
 
0.3%
0.4117647059 8
 
0.3%
0.2857142857 8
 
0.3%
Other values (2234) 2686
95.2%
ValueCountFrequency (%)
0.1180257511 1
< 0.1%
0.1234567901 1
< 0.1%
0.125 1
< 0.1%
0.1304347826 1
< 0.1%
0.132967033 1
< 0.1%
0.1339491917 1
< 0.1%
0.1354166667 1
< 0.1%
0.138576779 1
< 0.1%
0.1440588854 1
< 0.1%
0.1465567981 1
< 0.1%
ValueCountFrequency (%)
0.9677419355 1
< 0.1%
0.7955706985 1
< 0.1%
0.7796610169 1
< 0.1%
0.7421243705 1
< 0.1%
0.7368421053 1
< 0.1%
0.7323333333 1
< 0.1%
0.7272727273 1
< 0.1%
0.7147826087 1
< 0.1%
0.7114337568 1
< 0.1%
0.7107221007 1
< 0.1%

with_children
Real number (ℝ)

HIGH CORRELATION 

Distinct2158
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19434608
Minimum0
Maximum0.61728395
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size222.4 KiB
2024-05-31T16:43:53.945556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.071428571
Q10.13963855
median0.1857621
Q30.24032871
95-th percentile0.34375
Maximum0.61728395
Range0.61728395
Interquartile range (IQR)0.10069016

Descriptive statistics

Standard deviation0.082216024
Coefficient of variation (CV)0.42303928
Kurtosis0.91997065
Mean0.19434608
Median Absolute Deviation (MAD)0.051080002
Skewness0.66620484
Sum548.05593
Variance0.0067594745
MonotonicityNot monotonic
2024-05-31T16:43:54.017590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1666666667 17
 
0.6%
0.2 15
 
0.5%
0.25 14
 
0.5%
0.125 13
 
0.5%
0.1538461538 11
 
0.4%
0.2105263158 10
 
0.4%
0.1428571429 10
 
0.4%
0.1578947368 10
 
0.4%
0.2142857143 9
 
0.3%
0.1176470588 8
 
0.3%
Other values (2148) 2703
95.9%
ValueCountFrequency (%)
0 5
0.2%
0.01428571429 1
 
< 0.1%
0.01851851852 1
 
< 0.1%
0.01960784314 1
 
< 0.1%
0.02040816327 1
 
< 0.1%
0.02127659574 1
 
< 0.1%
0.02272727273 1
 
< 0.1%
0.02380952381 1
 
< 0.1%
0.025 1
 
< 0.1%
0.02631578947 1
 
< 0.1%
ValueCountFrequency (%)
0.6172839506 1
< 0.1%
0.5917159763 1
< 0.1%
0.5878023134 1
< 0.1%
0.5107296137 1
< 0.1%
0.5052083333 1
< 0.1%
0.4809707667 1
< 0.1%
0.4790697674 1
< 0.1%
0.4711246201 1
< 0.1%
0.46799117 1
< 0.1%
0.4674556213 1
< 0.1%

pensioner_households
Real number (ℝ)

HIGH CORRELATION 

Distinct2279
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41652151
Minimum0
Maximum0.78947368
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size222.4 KiB
2024-05-31T16:43:54.092303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.22181512
Q10.33619048
median0.42101321
Q30.5
95-th percentile0.59770115
Maximum0.78947368
Range0.78947368
Interquartile range (IQR)0.16380952

Descriptive statistics

Standard deviation0.11609326
Coefficient of variation (CV)0.27872092
Kurtosis-0.026971026
Mean0.41652151
Median Absolute Deviation (MAD)0.079867456
Skewness-0.11894747
Sum1174.5906
Variance0.013477644
MonotonicityNot monotonic
2024-05-31T16:43:54.195153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 27
 
1.0%
0.5454545455 10
 
0.4%
0.6666666667 9
 
0.3%
0.4 9
 
0.3%
0.5714285714 8
 
0.3%
0.5555555556 8
 
0.3%
0.5227272727 7
 
0.2%
0.4117647059 7
 
0.2%
0.5161290323 7
 
0.2%
0.5333333333 7
 
0.2%
Other values (2269) 2721
96.5%
ValueCountFrequency (%)
0 1
< 0.1%
0.01342281879 1
< 0.1%
0.02263822377 1
< 0.1%
0.0447761194 1
< 0.1%
0.06398448861 1
< 0.1%
0.06569343066 1
< 0.1%
0.07852852853 1
< 0.1%
0.08076422058 1
< 0.1%
0.08872854914 1
< 0.1%
0.0962962963 1
< 0.1%
ValueCountFrequency (%)
0.7894736842 1
< 0.1%
0.7727272727 1
< 0.1%
0.7666666667 2
0.1%
0.7647058824 1
< 0.1%
0.7619047619 1
< 0.1%
0.76 1
< 0.1%
0.7592592593 1
< 0.1%
0.75 1
< 0.1%
0.7473684211 1
< 0.1%
0.7368421053 1
< 0.1%

owner_occupied
Real number (ℝ)

HIGH CORRELATION 

Distinct2150
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80044732
Minimum0
Maximum1
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size222.4 KiB
2024-05-31T16:43:54.273246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.44319548
Q10.73154362
median0.86214095
Q30.91755609
95-th percentile0.95956939
Maximum1
Range1
Interquartile range (IQR)0.18601247

Descriptive statistics

Standard deviation0.16497852
Coefficient of variation (CV)0.20610791
Kurtosis2.4406439
Mean0.80044732
Median Absolute Deviation (MAD)0.073980681
Skewness-1.5621824
Sum2257.2614
Variance0.027217913
MonotonicityNot monotonic
2024-05-31T16:43:54.346866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9230769231 19
 
0.7%
0.9090909091 16
 
0.6%
0.9285714286 14
 
0.5%
0.9 14
 
0.5%
0.8823529412 14
 
0.5%
0.875 13
 
0.5%
0.9333333333 12
 
0.4%
0.9444444444 11
 
0.4%
0.8888888889 11
 
0.4%
0.9411764706 11
 
0.4%
Other values (2140) 2685
95.2%
ValueCountFrequency (%)
0 2
0.1%
0.005813953488 1
< 0.1%
0.01550387597 1
< 0.1%
0.02631578947 1
< 0.1%
0.02676399027 1
< 0.1%
0.05035971223 1
< 0.1%
0.05833696125 1
< 0.1%
0.06434782609 1
< 0.1%
0.0734939759 1
< 0.1%
0.09885386819 1
< 0.1%
ValueCountFrequency (%)
1 10
0.4%
0.9936708861 1
 
< 0.1%
0.9912663755 1
 
< 0.1%
0.9902912621 1
 
< 0.1%
0.9893617021 1
 
< 0.1%
0.9892473118 2
 
0.1%
0.9880952381 1
 
< 0.1%
0.9861111111 1
 
< 0.1%
0.9850746269 1
 
< 0.1%
0.9846153846 1
 
< 0.1%

rented
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2060
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17306105
Minimum0
Maximum1
Zeros63
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size222.4 KiB
2024-05-31T16:43:54.418284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.016129032
Q10.048780488
median0.10344828
Q30.24912184
95-th percentile0.54911158
Maximum1
Range1
Interquartile range (IQR)0.20034136

Descriptive statistics

Standard deviation0.17200638
Coefficient of variation (CV)0.99390579
Kurtosis2.19044
Mean0.17306105
Median Absolute Deviation (MAD)0.072198276
Skewness1.545979
Sum488.03215
Variance0.029586193
MonotonicityNot monotonic
2024-05-31T16:43:54.492472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
 
2.2%
0.1 15
 
0.5%
0.05263157895 14
 
0.5%
0.05882352941 13
 
0.5%
0.02702702703 13
 
0.5%
0.04081632653 12
 
0.4%
0.05 12
 
0.4%
0.02777777778 12
 
0.4%
0.07692307692 11
 
0.4%
0.06666666667 10
 
0.4%
Other values (2050) 2645
93.8%
ValueCountFrequency (%)
0 63
2.2%
0.004366812227 1
 
< 0.1%
0.005524861878 1
 
< 0.1%
0.006097560976 1
 
< 0.1%
0.006329113924 1
 
< 0.1%
0.007092198582 1
 
< 0.1%
0.008196721311 1
 
< 0.1%
0.00826446281 1
 
< 0.1%
0.008547008547 1
 
< 0.1%
0.008658008658 1
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9941860465 1
< 0.1%
0.984496124 1
< 0.1%
0.9683698297 1
< 0.1%
0.966442953 1
< 0.1%
0.9407923378 1
< 0.1%
0.932173913 1
< 0.1%
0.9180722892 1
< 0.1%
0.9011461318 1
< 0.1%
0.8956834532 1
< 0.1%

small_houses_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct1979
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82620391
Minimum0
Maximum1
Zeros13
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size222.4 KiB
2024-05-31T16:43:54.563869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.14942762
Q10.80670985
median0.95465966
Q30.97985309
95-th percentile0.99801252
Maximum1
Range1
Interquartile range (IQR)0.17314325

Descriptive statistics

Standard deviation0.26195023
Coefficient of variation (CV)0.31705277
Kurtosis2.3046658
Mean0.82620391
Median Absolute Deviation (MAD)0.03367934
Skewness-1.8579773
Sum2329.895
Variance0.068617924
MonotonicityNot monotonic
2024-05-31T16:43:54.637332image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 140
 
5.0%
0.9756097561 15
 
0.5%
0 13
 
0.5%
0.9772727273 13
 
0.5%
0.9841269841 11
 
0.4%
0.9714285714 10
 
0.4%
0.9655172414 9
 
0.3%
0.9814814815 9
 
0.3%
0.96875 9
 
0.3%
0.987804878 9
 
0.3%
Other values (1969) 2582
91.6%
ValueCountFrequency (%)
0 13
0.5%
0.0002194105171 1
 
< 0.1%
0.0002431709492 1
 
< 0.1%
0.0006574621959 1
 
< 0.1%
0.001439736734 1
 
< 0.1%
0.001670998886 1
 
< 0.1%
0.001859888407 1
 
< 0.1%
0.001953125 1
 
< 0.1%
0.002495651516 1
 
< 0.1%
0.002791563275 1
 
< 0.1%
ValueCountFrequency (%)
1 140
5.0%
0.9994660972 1
 
< 0.1%
0.9979360165 1
 
< 0.1%
0.9979013641 1
 
< 0.1%
0.9974226804 1
 
< 0.1%
0.996835443 1
 
< 0.1%
0.9966216216 1
 
< 0.1%
0.9965397924 1
 
< 0.1%
0.9964788732 1
 
< 0.1%
0.9963833635 1
 
< 0.1%

apartments_ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1493
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15134491
Minimum0
Maximum1
Zeros1249
Zeros (%)44.3%
Negative0
Negative (%)0.0%
Memory size222.4 KiB
2024-05-31T16:43:54.710691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0122704
Q30.17204029
95-th percentile0.83308935
Maximum1
Range1
Interquartile range (IQR)0.17204029

Descriptive statistics

Standard deviation0.26366571
Coefficient of variation (CV)1.7421512
Kurtosis2.3209061
Mean0.15134491
Median Absolute Deviation (MAD)0.0122704
Skewness1.8718698
Sum426.79263
Variance0.069519605
MonotonicityNot monotonic
2024-05-31T16:43:54.784449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1249
44.3%
0.02127659574 4
 
0.1%
0.009090909091 4
 
0.1%
1 3
 
0.1%
0.02202643172 3
 
0.1%
0.01351351351 3
 
0.1%
0.01315789474 3
 
0.1%
0.01388888889 3
 
0.1%
0.01282051282 3
 
0.1%
0.04 3
 
0.1%
Other values (1483) 1542
54.7%
ValueCountFrequency (%)
0 1249
44.3%
0.001436781609 1
 
< 0.1%
0.001449275362 1
 
< 0.1%
0.001672240803 1
 
< 0.1%
0.001774622893 1
 
< 0.1%
0.002016129032 1
 
< 0.1%
0.002360346184 1
 
< 0.1%
0.002392344498 1
 
< 0.1%
0.002475247525 1
 
< 0.1%
0.002570694087 1
 
< 0.1%
ValueCountFrequency (%)
1 3
0.1%
0.9991353221 1
 
< 0.1%
0.9986465383 1
 
< 0.1%
0.9967346939 1
 
< 0.1%
0.9959915612 1
 
< 0.1%
0.9956480605 1
 
< 0.1%
0.9951749095 1
 
< 0.1%
0.9951317297 1
 
< 0.1%
0.9950690335 1
 
< 0.1%
0.993940924 1
 
< 0.1%

Interactions

2024-05-31T16:43:52.781189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:48.828460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.417981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.863769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.373944image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.842184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.344411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.881902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.321415image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.835184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:48.929662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.467872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.916273image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.427527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.899718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.397993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.934737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.375034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.884381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.041202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.514383image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.968698image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.477020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.956784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.447485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.981759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.425841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.933514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.101363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.562412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.014734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.527686image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.007599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.495809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.027280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.474973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.985201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.154425image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.614198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.064639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.579899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.061777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.547064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.076886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.526089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:53.044866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.210521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.669147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.184188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.636990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.120551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.603907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.130256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.580559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:53.096426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.260491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.718992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.231280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.688472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.173006image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.652001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.177766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.630744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:53.146457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.311350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.766789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.278013image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.739198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.229778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.700907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.224782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.682563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:53.197224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.361921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:49.815660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.326220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:50.789748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.287783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:51.751970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.272388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-31T16:43:52.732626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-05-31T16:43:54.836632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
average_size_of_householdsaverage_floor_area_per_personone_person_householdswith_childrenpensioner_householdsowner_occupiedrentedsmall_houses_ratioapartments_ratio
average_size_of_households1.000-0.124-0.9010.838-0.3070.480-0.4350.421-0.335
average_floor_area_per_person-0.1241.000-0.063-0.3900.6400.526-0.5590.503-0.592
one_person_households-0.901-0.0631.000-0.6800.173-0.6150.573-0.5300.448
with_children0.838-0.390-0.6801.000-0.6200.139-0.0730.0870.030
pensioner_households-0.3070.6400.173-0.6201.0000.331-0.3970.357-0.478
owner_occupied0.4800.526-0.6150.1390.3311.000-0.9650.778-0.769
rented-0.435-0.5590.573-0.073-0.397-0.9651.000-0.7920.796
small_houses_ratio0.4210.503-0.5300.0870.3570.778-0.7921.000-0.891
apartments_ratio-0.335-0.5920.4480.030-0.478-0.7690.796-0.8911.000
2024-05-31T16:43:54.930154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
apartments_ratioaverage_floor_area_per_personaverage_size_of_householdsone_person_householdsowner_occupiedpensioner_householdsrentedsmall_houses_ratiowith_children
apartments_ratio1.000-0.592-0.3350.448-0.769-0.4780.796-0.8910.030
average_floor_area_per_person-0.5921.000-0.124-0.0630.5260.640-0.5590.503-0.390
average_size_of_households-0.335-0.1241.000-0.9010.480-0.307-0.4350.4210.838
one_person_households0.448-0.063-0.9011.000-0.6150.1730.573-0.530-0.680
owner_occupied-0.7690.5260.480-0.6151.0000.331-0.9650.7780.139
pensioner_households-0.4780.640-0.3070.1730.3311.000-0.3970.357-0.620
rented0.796-0.559-0.4350.573-0.965-0.3971.000-0.792-0.073
small_houses_ratio-0.8910.5030.421-0.5300.7780.357-0.7921.0000.087
with_children0.030-0.3900.838-0.6800.139-0.620-0.0730.0871.000

Missing values

2024-05-31T16:43:53.266226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-31T16:43:53.350221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

average_size_of_householdsaverage_floor_area_per_personone_person_householdswith_childrenpensioner_householdsowner_occupiedrentedsmall_houses_ratioapartments_ratio
postcodenamemunicipality
00100Helsinki keskusta - Etu-T��l�Helsinki1.739.70.5298640.1260700.2380350.4761670.5188720.0002430.949907
00120Punavuori - BulevardiHelsinki1.840.10.5122790.1512770.2303540.5159630.4756880.0014400.944262
00130KaartinkaupunkiHelsinki1.944.20.4498380.1704420.2502700.4940670.4973030.0000000.845893
00140Kaivopuisto - UllanlinnaHelsinki1.842.50.5203460.1484430.2682430.5228460.4687430.0016710.993873
00150Punavuori - Eira - HernesaariHelsinki1.635.40.6058810.1202370.2093270.4986950.4924310.0041400.988910
00160KatajanokkaHelsinki1.940.20.4297580.1782480.3311930.4992450.4629910.0006570.995069
00170KruununhakaHelsinki1.839.10.4900600.1550700.2460240.5278330.4659540.0018600.944203
00180Kamppi - RuoholahtiHelsinki1.833.90.5100560.1566930.2086370.3837140.6124610.0071910.983983
00190SuomenlinnaHelsinki2.431.80.3129500.3741010.2302160.0503600.8956830.1342110.807895
00200Pohjois-LauttasaariHelsinki1.833.80.5162390.1925960.2409720.5697250.4259600.0504920.925972
average_size_of_householdsaverage_floor_area_per_personone_person_householdswith_childrenpensioner_householdsowner_occupiedrentedsmall_houses_ratioapartments_ratio
postcodenamemunicipality
99770Jeesi�Sodankyl�1.944.90.3928570.0714290.5535710.8392860.0892861.0000000.000000
99800IvaloInari2.042.40.4140930.1652890.4019140.6441930.3227490.8452430.102555
99830Saariselk�Inari1.838.90.5049500.1386140.1732670.3712870.6138610.7027030.227027
99860NellimInari1.748.00.5061730.0864200.6049380.7654320.1851851.0000000.000000
99870Inari Keskus-LemmenjokiInari2.141.00.4218180.2127270.4254550.6745450.2890910.8975820.041252
99910Kaamanen-PartakkoInari1.650.20.5517240.0689660.5632180.5977010.2413790.8823530.008403
99930Sevettij�rvi-N��t�m�Inari1.948.90.4649120.0701750.5087720.9210530.0701750.9463090.040268
99950KarigasniemiUtsjoki1.949.70.4933330.1800000.4200000.6600000.2666670.9585490.025907
99980Utsjoki KeskusUtsjoki1.945.90.5229890.1408050.4827590.6091950.3390800.9088940.006508
99990NuorgamUtsjoki2.044.60.4285710.2087910.4395600.6153850.2967030.8604650.000000